MicroRNA-196a links human body fat distribution to adipose tissue extracellular matrix composition

Abstract Background: Abdominal fat mass is associated with metabolic risk whilst gluteal femoral fat is paradoxically protective. MicroRNAs are known to be necessary for adipose tissue formation and function but their role in regulating human fat distribution remains largely unexplored. Methods: An initial microarray screen of abdominal subcutaneous and gluteal adipose tissue, with validatory qPCR, identified microRNA-196a as being strongly differentially expressed between gluteal and abdominal subcutaneous adipose tissue. Findings: We found that rs11614913, a SNP within pre-miR-196a-2 at the HOXC locus, is an eQTL for miR-196a expression in abdominal subcutaneous adipose tissue (ASAT). Observations in large cohorts showed that rs11614913 increased waist-to-hip ratio, which was driven specifically by an expansion in ASAT. In further experiments, rs11614913 was associated with adipocyte size. Functional studies and transcriptomic profiling of miR-196a knock-down pre-adipocytes revealed a role for miR-196a in regulating pre-adipocyte proliferation and extracellular matrix pathways. Interpretation: These data identify a role for miR-196a in regulating human body fat distribution.: This work was supported by the Medical Research Council and Novo Nordisk UK Research Foundation (G1001959) and Swedish Research Council. We acknowledge the OBB-NIHR Oxford Biomedical Research Centre and the British Heart Foundation (BHF) (RG/17/1/32663). Work performed at the MRC Epidemiology Unit was funded by the United Kingdom's Medical Research Council through grants MC_UU_12015/1, MC_PC_13046, MC_PC_13048 and MR/L00002/1

Assessing the causal association of glycine with risk of cardio-metabolic diseases

Circulating levels of glycine have previously been associated with lower incidence of coronary heart disease (CHD) and type 2 diabetes (T2D) but it remains uncertain if glycine plays an aetiological role. We present a meta-analysis of genome-wide association studies for glycine in 80,003 participants and investigate the causality and potential mechanisms of the association between glycine and cardio-metabolic diseases using genetic approaches. We identify 27 genetic loci, of which 22 have not previously been reported for glycine. We show that glycine is genetically associated with lower CHD risk and find that this may be partly driven by blood pressure. Evidence for a genetic association of glycine with T2D is weaker, but we find a strong inverse genetic effect of hyperinsulinaemia on glycine. Our findings strengthen evidence for a protective effect of glycine on CHD and show that the glycine-T2D association may be driven by a glycine-lowering effect of insulin resistance

Rare and common genetic determinants of metabolic individuality and their effects on human health

Garrod’s concept of ‘chemical individuality’ has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant–metabolite associations (P < 1.25 × 10−11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant–metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships

Identification of rare loss-of-function genetic variation regulating body fat distribution

This is the final version. Available on open access from Oxford University Press via the DOI in this recordData Availability: This research was conducted using the UK Biobank resource (application Nos. 44448 and 9905). Access to the UK Biobank genotype and phenotype data is open to all approved health researchers (http://www.ukbiobank.ac.uk/).CONTEXT: Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly non-coding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss-of-function (LoF) would be of most therapeutic benefit. OBJECTIVE, DESIGN AND SETTING: To identify genes/proteins involved in determining fat distribution, we combined the power of genome-wide analysis of array-based rare, non-synonymous variants in 450,562 individuals of UK Biobank with exome-sequence-based rare loss of function gene burden testing in 184,246 individuals. RESULTS: The data indicates that loss-of-function of four genes (PLIN1 [LoF variants, p=5.86×10 -7], INSR [LoF variants, p=6.21×10 -7], ACVR1C [LoF + Moderate impact variants, p=1.68×10 -7; Moderate impact variants, p=4.57×10 -7] and PDE3B [LoF variants, p=1.41×10 -6]) is associated with a beneficial impact on WHRadjBMI and increased gluteofemoral fat mass, whereas LoF of PLIN4 [LoF variants, p=5.86×10 -7] adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B and ACVR1C favourably affects metabolic phenotypes (e.g. triglyceride [TG] and HDL cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. CONCLUSION: This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counter-intuitive insight into the potential consequences of targeting these molecules therapeutically.Medical Research Council (MRC)National Institute for Health Research (NIHR)Wellcome TrustResearch Englan

Rare and common genetic determinants of metabolic individuality and their effects on human health

Garrod’s concept of ‘chemical individuality’ has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant–metabolite associations (P < 1.25 × 10−11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant–metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships

Prioritising risk factors for type 2 diabetes: causal inference through genetic approaches

Purpose of the Review Causality has been demonstrated for few of the many putative risk factors for type 2 diabetes (T2D) emerging from observational epidemiology. Genetic approaches are increasingly being used to infer causality, and in this review, we discuss how genetic discoveries have shaped our understanding of the causal role of factors associated with T2D. Recent Findings Genetic discoveries have led to the identification of novel potential aetiological factors of T2D, including the protective role of peripheral fat storage capacity and specific metabolic pathways, such as the branched-chain amino acid breakdown. Consideration of specific genetic mechanisms contributing to overall lipid levels has suggested that distinct physiological processes influencing lipid levels may influence diabetes risk differentially. Genetic approaches have also been used to investigate the role of T2D and related metabolic traits as causal risk factors for other disease outcomes, such as cancer, but comprehensive studies are lacking. Summary Genome-wide association studies of T2D and metabolic traits coupled with high-throughput molecular phenotyping and in-depth characterisation and follow-up of individual loci have provided better understanding of aetiological factors contributing to T2D

Prioritising risk factors for type 2 diabetes: causal inference through genetic approaches

Purpose of the Review Causality has been demonstrated for few of the many putative risk factors for type 2 diabetes (T2D) emerging from observational epidemiology. Genetic approaches are increasingly being used to infer causality, and in this review, we discuss how genetic discoveries have shaped our understanding of the causal role of factors associated with T2D. Recent Findings Genetic discoveries have led to the identification of novel potential aetiological factors of T2D, including the protective role of peripheral fat storage capacity and specific metabolic pathways, such as the branched-chain amino acid breakdown. Consideration of specific genetic mechanisms contributing to overall lipid levels has suggested that distinct physiological processes influencing lipid levels may influence diabetes risk differentially. Genetic approaches have also been used to investigate the role of T2D and related metabolic traits as causal risk factors for other disease outcomes, such as cancer, but comprehensive studies are lacking. Summary Genome-wide association studies of T2D and metabolic traits coupled with high-throughput molecular phenotyping and in-depth characterisation and follow-up of individual loci have provided better understanding of aetiological factors contributing to T2D

Large scale phenotype imputation and in vivo functional validation implicate ADAMTS14 as an adiposity gene.

Obesity remains an unmet global health burden. Detrimental anatomical distribution of body fat is a major driver of obesity-mediated mortality risk and is demonstrably heritable. However, our understanding of the full genetic contribution to human adiposity is incomplete, as few studies measure adiposity directly. To address this, we impute whole-body imaging adiposity phenotypes in UK Biobank from the 4,366 directly measured participants onto the rest of the cohort, greatly increasing our discovery power. Using these imputed phenotypes in 392,535 participants yielded hundreds of genome-wide significant associations, six of which replicate in independent cohorts. The leading causal gene candidate, ADAMTS14, is further investigated in a mouse knockout model. Concordant with the human association data, the Adamts14 &lt;sup&gt;-/-&lt;/sup&gt; mice exhibit reduced adiposity and weight-gain under obesogenic conditions, alongside an improved metabolic rate and health. Thus, we show that phenotypic imputation at scale offers deeper biological insights into the genetics of human adiposity that could lead to therapeutic targets

MicroRNA-196a links human body fat distribution to adipose tissue extracellular matrix composition

Background Abdominal fat mass is associated with metabolic risk whilst gluteal femoral fat is paradoxically protective. MicroRNAs are known to be necessary for adipose tissue formation and function but their role in regulating human fat distribution remains largely unexplored. Methods An initial microarray screen of abdominal subcutaneous and gluteal adipose tissue, with validatory qPCR, identified microRNA-196a as being strongly differentially expressed between gluteal and abdominal subcutaneous adipose tissue. Findings We found that rs11614913, a SNP within pre-miR-196a-2 at the HOXC locus, is an eQTL for miR-196a expression in abdominal subcutaneous adipose tissue (ASAT). Observations in large cohorts showed that rs11614913 increased waist-to-hip ratio, which was driven specifically by an expansion in ASAT. In further experiments, rs11614913 was associated with adipocyte size. Functional studies and transcriptomic profiling of miR-196a knock-down pre-adipocytes revealed a role for miR-196a in regulating pre-adipocyte proliferation and extracellular matrix pathways. Interpretation These data identify a role for miR-196a in regulating human body fat distribution. Fund This work was supported by the Medical Research Council and Novo Nordisk UK Research Foundation (G1001959) and Swedish Research Council. We acknowledge the OBB-NIHR Oxford Biomedical Research Centre and the British Heart Foundation (BHF) (RG/17/1/32663). Work performed at the MRC Epidemiology Unit was funded by the United Kingdom's Medical Research Council through grants MC_UU_12015/1, MC_PC_13046, MC_PC_13048 and MR/L00002/1